User profiles are of interest in various situations in order to obtain information about various types of consumer behaviour. It is for example of interest to obtain such profiles in relation to the shopping habits of a user in retail stores, like supermarkets.
Another area, where user profiles are of interest is in the field of communication, like for instance wireless communication. Here the user profile, for instance the way a user uses a communication network, is of interest for various reasons, such as billing and identifying services to be offered to the user, but also in relation to how the network can be managed and dimensioned.
The user profile is then typically determined based on data collected in the environment of the user. More particularly the user profile is typically based on patterns in the collected data. Based on a large amount of data it is then possible to obtain a pattern in the data using for instance machine learning. One or more such patterns are then used for obtaining a user profile. Examples on machine learning techniques include support vector machines and principal component analysis.
These types of techniques are useful when obtaining a user profile based on data. However if the data on which a profile is to be made changes over time, in that new data types are introduced, then the accuracy of older profiles that are based on older data is unreliable and the older profile may therefore be risky to use.
The present invention is directed towards allowing continued use of an existing profile.